• 대한치과교정학회지
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• 제44권2호
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• pp.69-76
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• 2014

Objective: This study aimed to evaluate the accuracy and precision of polyurethane (PUT) dental arch models fabricated using a three-dimensional (3D) subtractive rapid prototyping (RP) method with an intraoral scanning technique by comparing linear measurements obtained from PUT models and conventional plaster models. Methods: Ten plaster models were duplicated using a selected standard master model and conventional impression, and 10 PUT models were duplicated using the 3D subtractive RP technique with an oral scanner. Six linear measurements were evaluated in terms of x, y, and z-axes using a non-contact white light scanner. Accuracy was assessed using mean differences between two measurements, and precision was examined using four quantitative methods and the Bland-Altman graphical method. Repeatability was evaluated in terms of intra-examiner variability, and reproducibility was assessed in terms of interexaminer and inter-method variability. Results: The mean difference between plaster models and PUT models ranged from 0.07 mm to 0.33 mm. Relative measurement errors ranged from 2.2% to 7.6% and intraclass correlation coefficients ranged from 0.93 to 0.96, when comparing plaster models and PUT models. The Bland-Altman plot showed good agreement. Conclusions: The accuracy and precision of PUT dental models for evaluating the performance of oral scanner and subtractive RP technology was acceptable. Because of the recent improvements in block material and computerized numeric control milling machines, the subtractive RP method may be a good choice for dental arch models.

• 한국경영과학회지
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• 제1권1호
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• pp.151-170
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• 1976

There are many cases of production processes which intermittently produce several different kinds of products for stock through one set of physical facility. In this case, an important question is what size of production run should be prduced once we do set-up for a product in order to minimize the total cost, that is, the sum of the set-up, carrying, and stock-out costs. This problem is used to be called scheduling of multiple products through a single facility in the production management field. Despite the very common occurrence of this type of production process, no one has yet devised a method for determining the optimal production schedule. The purpose of this study is to develop quantitative analytical models which can be used practically and give us rational production schedules. The study is to show improved models with application to a can-manufacturing plant. In this thesis the economic production quantity (EPQ) model was used as a basic model to develop quantitative analytical models for this scheduling problem and two cases, one with stock-out cost, the other without stock-out cost, were taken into consideration. The first analytical model was developed for the scheduling of products through a single facility. In this model we calculate No, the optimal number of production runs per year, minimizing the total annual cost above all. Next we calculate No$_{i}$ is significantly different from No, some manipulation of the schedule can be made by trial and error in order to try to fit the product into the basic (No schedule either more or less frequently as dictated by) No$_{i}$, But this trial and error schedule is thought of inefficient. The second analytical model was developed by reinterpretation by reinterpretation of the calculating process of the economic production quantity model. In this model we obtained two relationships, one of which is the relationship between optimal number of set-ups for the ith item and optimal total number of set-ups, the other is the relationship between optimal average inventory investment for the ith item and optimal total average inventory investment. From these relationships we can determine how much average inventory investment per year would be required if a rational policy based on m No set-ups per year for m products were followed and, alternatively, how many set-ups per year would be required if a rational policy were followed which required an established total average inventory inventory investment. We also learned the relationship between the number of set-ups and the average inventory investment takes the form of a hyperbola. But, there is no reason to say that the first analytical model is superior to the second analytical model. It can be said that the first model is useful for a basic production schedule. On the other hand, the second model is efficient to get an improved production schedule, in a sense of reducing the total cost. Another merit of the second model is that, unlike the first model where we have to know all the inventory costs for each product, we can obtain an improved production schedule with unknown inventory costs. The application of these quantitative analytical models to PoHang can-manufacturing plants shows this point.int.

• 대한산업공학회지
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• 제5권2호
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• pp.21-36
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• 1979

Static muscle contractions when prolonged or frequently repeated result in discomfort, fatigue, and musculosketal injuries. An analytic and quantitative model has been developed in order to expand the working knowledge on muscle fatigue. In this paper, three Markov models of muscle fatigue are developed. These models are based on motor unit fatigue-recovery characteristics obtained from information on motor unit behavior as it relates to fatigue and graded exertions. Three successively more realistic models are developed that involve: (1) homogeneous motor units with intensity-dependent fatigue rates and state-independent recovery rates (the HMSI model); (2) homogeneous motor units, intensity-dependent fatigue rates and state-dependent recovery rates (the HMSD model); and (3) non-homogeneous motor units (i.e., Type S and Type F), intensity-dependent fatigue rates and state-dependent recovery rates (the HMSD model). The result indicate that a simple stochastic model provide a means to analyze the complex nature of muscle fatigue in sequential static exertions.

• Genomics & Informatics
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• 제14권4호
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• pp.149-159
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• 2016

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

• 한국정밀공학회지
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• 제17권4호
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• pp.209-219
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• 2000

A method is developed to evaluate machining process and to determine the optimal machining conditions considering the environmental effects. The method Is based on the evaluation attributes from the general LCA programs and the analysis technique of AHP from HHS. To assist the analysis. the mass models of cutting energy, tools, and fluids are developed. The models may be used for both quantitative prediction of the uses and disposed masses of materials and optimization of the machining conditions. The algorithm with the mass models is applied to the milling process planning. The process to survey the environmental data, calculate the used mass, and evaluate the alternatives is demonstrated. This demonstration illustrates the of the change of process conditions of the decision making.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2000년도 추계학술발표회 논문집
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• pp.5-12
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• 2000

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. We consider hierarchical models including selection models under a skewed heavy tailed error distribution and it is shown to be useful in such Bayesian meta-analysis. A general class of skewed elliptical distribution is reviewed and developed. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierarchical selection model and use Markov chain Monte Carlo methods to develop inference for the parameters of interest.

• 경영과학
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• 제20권2호
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• pp.135-149
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• 2003

Developing a proper program for customer evaluation is one of the most imminent tasks to implement CRM (Customer Relationship Management). Design of the Customer Value model is an important key to the customer evaluation progrgm. This paper proposes two models for estimating Customer Value. The first one is a Description Model for Customer Value based on customer CSI (Customer Satisfaction Index) data. This model represents as quantitative numbers what customers feel from the company or the service. The second one is a Prediction Model which employs factor analysis and regression to predict customer value. This paper exploits the two models to evaluate Customer Value as well as for customer behavior prediction.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2005년도 추계 학술대회논문집
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• pp.183-186
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• 2005

We developed an upwind numerical formulation based on the eigenvalues of the approximate Jacobian matrix in order to solve the hyperbolic conservation laws governing the two-fluid two-phase flow models. We obtained eight analytic eigenvalues in the two dimensions that can be used for estimate of the wave speeds essential in constructing an upwind numerical method. Two-dimensional underwater cavitation in a flow past structural shapes or by underwater explosion can be solved using this method. We present quantitative prediction of cavitation for the water tunnel wall and airfoils that has both experimental data as well as numerical results by other numerical methods and models.

• 한국경영과학회지
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• 제20권1호
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• pp.85-99
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• 1995

Increasingly, risk analysis is becoming important ingredients in achieving the successful implementation and application in the area of the project management. The project management system is designed to manage or control the project resources on a given activity within time, cost and performance so called TPPM (Total Productive Project Management). In this research, a risk analysis model misproposed to identify potential problem areas, quantify the risks, and generated the chice of the action that can be taken to reduce the risk. In addition two analysis models are proposed : 1) risk factor model and 2) network simulation model using VERT (Venture Evaluation and Review Technique ). The objective of the remodels is to estimate the schedule, cost performance risks. These proposed quantitative models for project risk analysis are proving its value for the project managers who need to assess the risk of changes in cost, schedule, or performance. The proposed models will be used in the area of project selection, evaluation and the allocation of project resources.

• 한국미생물생명공학회:학술대회논문집
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• 한국미생물생명공학회 2005년도 2005 Annual Meeting & International Symposium
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• pp.54-62
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• 2005

While genomics (the set of experimental and computational tools that allows the blueprints of life to be read) opens the doors to a more rational approach to the design and use of living cells to bring about desirable chemical transformations, genomics is, by itself, insufficient. We need tools that allow us to relate genomic and molecular information to cellular physiology and then to the response of a population of cells. We propose the development of hybrid computer cellular models. In such models genomics and chemical detail for a cellular subsystem (e.g. pathogenesis) is embedded in a coarse-grain cell model. Such a construct allows the quantitative and explicit linkage of genomic detail to cell physiology to the extracellular environment. To illustrate the principles involved we are constructing a model for a minimal cell. A minimal cell is a bacterial cell with the fewest number of genes necessary to sustain life as a free living microbe.